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There is an ample genetic diversity of plants with medicinal importance around the globe and this pool of genetic variation serves as the base for selection as well as for plant improvement. Thus, identification, characterization and documentation of the gene pool of medicinal plants are essential for this purpose. Genomic information of many a medicinal plant species has increased rapidly since the past decade and genetic resources available for domestication and improvement programs include genome sequencing, expressed sequence tags sequencing, transcript profiling, gene transmit, molecular markers in favor of mapping and breeding. In recent years, multiple endeavors have been undertaken for genomic characterization of medicinal plant species with the aid of molecular markers for sustainable utilization of gene pool, its conservation and future studies. Recent advancement in genomics is so fast that only some researches have been published till date and to a large extent documentation is restricted to electronic resources. Whole genome profiling of the identified medicinal plant species, carried out by several researchers, based on the DNA fingerprinting, is well documented in the present review. This review will facilitate preparing a database of the widely used, economically important medicinal plant species, based on their genomic organization.

We analyze data from 52 online in-game sports betting markets (where betting is allowed continuously throughout a game), including 34 markets based on soccer (European football) games from the 2002 World Cup, and 18 basketball games from the 2002 USA National Basketball Association (NBA) championship. We show that prices on average approach the correct outcome over time, and the price dynamics in the markets are closely coupled with game events, agreeing with efficient market assumptions. We also examine qualitative distinctions between the two types of games.

A peculiar bacterial growth was very often noticed in leaf-initiated tissue cultures of Sansevieriatrifasciata, a succulent belonging to the Asparagaceae family. The isolate left trails of some highly viscous material on the walls of the suspension vessels or developed a thick overlay on semisolid media without adversities in plant growth. FTIR identified this substance to be an extracellular polysaccharide. Various morphological, biochemical tests, and molecular analyses using 16S rRNA, atpD, and recA genes characterized this isolate JAS1 as a novel strain of Agrobacteriumpusense. Its mucoidal growth over Murashige and Skoog media yielded enormous exopolysaccharide (7252 mg l−1), while in nutrient agar it only developed fast-growing swarms. As a qualifying plant growth-promoting bacteria, it produces significant indole-3-acetic acid (86.95 mg l−1), gibberellic acid (172.98 mg l−1), ammonia (42.66 µmol ml−1). Besides, it produces siderophores, 1-aminocyclopropane-1-carboxylicaciddeaminase, fixes nitrogen, forms biofilms, and productively solubilizes soil inorganic phosphates, and zinc. Under various treatments with JAS1, wheat and chickpea resulted in significantly enhanced shoot and root growth parameters. PGP effects of JAS1 positively enhanced plants’ physiological growth parameters reflecting significant increments in overall chlorophyll, carotenoids, proline, phenols, flavonoids, and sugar contents. In addition, the isolated strain maintained both plant and soil health under an intermittent soil drying regime, probably by both its PGP and EPS production attributes, respectively.

In the scenario of global climate change, understanding how plants respond to drought is critical for developing future crops that face restricted water resources. This present study focuses on the role of WRKY transcription factors on drought tolerance in tomato, Solanum lycopersicum L., which is a significant vegetable crop. WRKY transcription factors are a group of proteins that regulate a wild range of growth and developmental processes in plants such as seed germination and dormancy and the stress response. These transcription factors are defined by the presence of a DNA-binding domain, namely, the WRKY domain. It is well-known that WRKY transcription factors can interact with a variety of proteins and therefore control downstream activities. It aims to simulate the effect of curcumin, a bioactive compound with regulatory capacity, on the protein–protein interaction events by WRKY transcription factors with an emphasis on drought stress. It was found that curcumin binds to WRKY with an energy of −11.43 kcal/mol with inhibitory concentration (Ki) 0.12 mM and has the potential to improve fruit quality and reinforce drought tolerance of S. lycopersicum, according to the results based on bioinformatics tools. The root means square deviation (RMSD) of the C-α, the backbone of 2AYD with ligand coupled complex, displayed a very stable structure with just a little variation of 1.89 Å. MD simulation trajectory of Cα atoms of 2AYD bound to Curcumin revealed more un-ordered orientation in PC1 and PC10 modes and more toward negative correlation from the initial 400 frames during PCA. Establishing the binding energies of the ligand–target interaction is essential in order to characterize the compound’s binding affinity to the drought transcription factor. We think we have identified a phyto-agent called curcumin that has the potential to enhance the drought tolerance. Compared to the part of the mismatch repair-base technique that can be used to fix drought related genes, curcumin performed better in a drop-in crop yield over time, and it was suggested that curcumin is a potential candidate factor for improving drought tolerance in tomatoes, and it needs future validation by experiments in laboratory and field.

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Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.

IntroductionDrought is the largest abiotic factor impacting agriculture. Plants are challenged by both natural and artificial stressors because they are immobile. To produce drought-resistant plants, we need to know how plants react to drought. A largescale proteome study of leaf and root tissue found drought-responsive proteins. Tomato as a vegetable is grown worldwide. Agricultural biotechnology focuses on creating drought-resistant cultivars. This is important because tomato drought is so widespread. Breeders have worked to improve tomato quality, production, and stress resistance. Conventional breeding approaches have only increased drought tolerance because of drought’s complexity. Many studies have examined how tomatoes handle drought. With genomics, transcriptomics, proteomics, metabolomics, and modern sequencing technologies, it’s easier to find drought-responsive genes.MethodBiotechnology and in silico studies has helped demonstrate the function of drought-sensitive genes and generate drought-resistant plant types. The latest tomato genome editing technology is another. WRKY genes are plant transcription factors. They help plants grow and respond to both natural and artificial stimuli. To make plants that can handle stress, we need to know how WRKY-proteins, which are transcription factors, work with other proteins and ligands in plant cells by molecular docking and modeling study.ResultAbscisic acid, a plant hormone generated in stressed roots, was used here to make plants drought-resistant. Abscisic acid binds WRKY with binding affinity -7.4kcal/mol and inhibitory concentration (Ki) 0.12 microM.DiscussionThis study aims to modulate the transcription factor so plants can handle drought and stress better. Therefore, polyphenols found to make Solanum lycopersicum more drought-tolerant.